• graphics. Defaults R plotting system. Fast for exploratory analysis. Nice graphics are constructed step by step using different calls.
• grid package based.
  • lattice. Fast plots for exploratory analysis. By default plots are nicer than base system. Tuning is difficult.
  • ggplot2. System implementing a layered grammar of graphics.

Fuel economy data from 1999 to 2008 for 38 popular models of cars:

library(tidyverse)
data(mpg, package='ggplot2')
mpg

## # A tibble: 234 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4           1.8  1999     4 auto… f        18    29 p     comp…
##  2 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
##  3 audi         a4           2    2008     4 manu… f        20    31 p     comp…
##  4 audi         a4           2    2008     4 auto… f        21    30 p     comp…
##  5 audi         a4           2.8  1999     6 auto… f        16    26 p     comp…
##  6 audi         a4           2.8  1999     6 manu… f        18    26 p     comp…
##  7 audi         a4           3.1  2008     6 auto… f        18    27 p     comp…
##  8 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
##  9 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
## 10 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
## # … with 224 more rows

 ggplot(data = <DATA>) +                        # INITIAL LAYER
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>)) +  # NEXT LAYER
  ⋮
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>)) +  # LAST LAYER
  <TUNNING>

• DATA: available variables
• GEOM_FUNCTION: what should be plotted
• MAPPINGS: relations between variables and aesthetics

• Mapping: displ \(\rightarrow\) x, hwy \(\rightarrow\) y

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy))

• Mapping: displ \(\rightarrow\) x, hwy \(\rightarrow\) y, class \(\rightarrow\) color

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

• Mapping: displ \(\rightarrow\) x, hwy \(\rightarrow\) y
• We can fix the value of aesthetics

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy), color = "#3366ff", shape = 15)

• Mapping: displ \(\rightarrow\) x, hwy \(\rightarrow\) y
• We can add more layers

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy)) +
  geom_smooth(mapping = aes(x = displ, y = hwy), method = 'lm')

• Mapping: displ \(\rightarrow\) x, hwy \(\rightarrow\) y
• We can add more layers

ggplot(data = mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  geom_smooth(method = 'lm')

ggplot(data = mpg, aes(x = displ, y = hwy, color = class)) +
  geom_point() +
  labs(title = "Bivariate plot",
       subtitle = "Relation between engine displacement and consumption",
       x = 'Engine displacement (liters)', y = 'Consumption (milles x gallon)', 
       color = 'Car class', caption = "Statistical Programming Course")

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + geom_smooth(method = 'lm') + 
  facet_wrap(~drv)

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + geom_smooth(method = 'lm') + 
  facet_grid(year~drv)

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + geom_smooth(method = 'lm') + 
  facet_grid(year~drv) +
  theme_minimal()

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + geom_smooth(method = 'lm') + 
  facet_grid(year~drv) +
  theme_minimal() +
  theme(strip.text = element_text(face = "bold.italic"), 
        strip.background = element_rect(fill = 'grey'))

# Check size with par('din')
ggsave(plot = p, filename =  "filename.pdf", width = 6, height = 4)
ggsave(plot = p, filename =  "filename.svg", width = 6, height = 4)

If plot parameter is omitted, last plot will be saved.

• https://ggplot2.tidyverse.org
• Cheat sheet html
• R Graphics Cookbook https://r-graphics.org/
• The R Graph Gallery https://www.r-graph-gallery.com/ggplot2-package.html
• gggplot2: Elegant Graphics for Data Analysis https://ggplot2-book.org/

• How is the distribution of origin?

ggplot(data=flights) +
  geom_bar(aes(x = origin))

ggplot(data=flights) +
  geom_bar(aes(x = origin, 
               y=(..count..)/sum(..count..)))

dtab = flights %>% count(origin) %>%
  mutate(p = sprintf("%0.1f%%", 100*prop.table(n)),
         cn = rev(cumsum(rev(n))),
         y = cn + diff(c(cn,0))/2 )

ggplot(data=flights) +
  geom_bar(aes(x="",y=(..count..), fill=origin)) +
  geom_text(data=dtab, aes(x="", y=y, label = p)) +
  coord_polar(theta = 'y', start = pi/2, direction = 1) +
  theme_void()

• How is the distribution of dep_delay?

ggplot() +
  geom_histogram(data=flights, aes(x=dep_delay), bins = 10)

• How is the distribution of dep_delay?

ggplot() +
  geom_histogram(data=flights, aes(x=dep_delay), breaks = c(-50, 0, 50, 200, 1500))

• How is the distribution of dep_delay?

ggplot() +
  geom_boxplot(data=flights, aes(x=dep_delay))

• How is the distribution of dep_delay?

ggplot() +
  geom_density(data=flights, aes(x=dep_delay), col=NA, fill = 'blue', alpha=0.4)

• How is the distribution of dep_delay?

ggplot() +
  geom_density(data=flights, aes(x=dep_delay), col=NA, fill = 'blue', alpha=0.4) +
  coord_cartesian(xlim = c(-30,60))

• How is the distribution of dep_delay?

ggplot() +
  geom_histogram(data=flights, aes(x=dep_delay), breaks=seq(-50,1500,5), 
                 fill='blue', alpha=0.4) +
  coord_cartesian(xlim = c(-30,60))

• How is the distribution of dep_delay and origin?

ggplot() +
  geom_boxplot(data=flights, aes(x=origin, y=dep_delay))

• How is the distribution of dep_delay and origin?

ggplot() +
  geom_density(data=flights, aes(x=dep_delay), col=NA, fill = 'blue', alpha=0.4) +
  coord_cartesian(xlim = c(-30,60)) +
  facet_wrap(~origin, ncol = 1)

• How is the distribution of dep_delay and origin?

ggplot() +
  geom_histogram(data=flights, aes(x=dep_delay), breaks=seq(-50,1500,5), 
                 fill='blue', alpha=0.4) +
  coord_cartesian(xlim = c(-30,60)) +
  facet_wrap(~origin, ncol = 1)

• How is the distribution of dep_delay and origin?

library(ggridges)
ggplot() +
  geom_density_ridges(data=flights, aes(x=dep_delay, y = origin), 
                      scale=2, col=NA, fill = 'blue', alpha=0.4) +
  coord_cartesian(xlim = c(-30,60))

flights = flights %>% 
  filter(!is.na(arr_delay)) %>%
  mutate(arrival = if_else(arr_delay > 0, 'delayed', 'on-time'))

• How is the distribution of origin and on.time

ggplot(data=flights) +
  geom_bar(aes(x=arrival, fill=origin))

flights_n = flights %>% count(origin, arrival)

• origin relative frequencies

dplot = group_by(flights_n, arrival) %>% mutate(p = prop.table(n))
ggplot(data=dplot) +
  geom_bar(aes(x=arrival, y=p, fill=origin), stat = 'identity')

flights_n = flights %>% count(origin, arrival)

• arrival relative frequencies

dplot = group_by(flights_n, origin) %>% mutate(p = prop.table(n))
ggplot(data=dplot) +
  geom_bar(aes(x=origin, y=p, fill=arrival), stat = 'identity')

• How is the distribution of dep_delay and arr_delay?

ggplot(data=flights) +
  geom_point(aes(x=dep_delay, y=arr_delay))

ggplot(data=flights) +
  geom_point(aes(x=dep_delay, y=arr_delay, alpha = ..n..), size = 1, stat = 'sum')

# Equivalent,
# ggplot(data=count(flights, dep_delay, arr_delay)) +
#   geom_point(aes(x=dep_delay, y=arr_delay, alpha = n), size = 1, stat = 'identity')

• ggthemes. https://github.com/jrnold/ggthemes
• ggtech. https://github.com/ricardo-bion/ggtech
• ggthemr: https://github.com/cttobin/ggthemr
• cowplot: https://cran.r-project.org/web/packages/cowplot/vignettes/introduction.html
• bbplot. https://bbc.github.io/rcookbook
• hrbrthemes: https://github.com/hrbrmstr/hrbrthemes

library(plotly)

p = ggplot(data = mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  geom_smooth(method = 'lm')

ggplotly(p)

library(gganimate)
library(gapminder)

p <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  facet_wrap(~continent) +
  # Here comes the gganimate specific bits
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')

animate(p, nframes = 20, fps = 5, width = 500, height=400)

• https://www.tylermw.com/3d-ggplots-with-rayshader/

BUT REMEMBER:

• Don’t use gratuitous 3D
• It’s difficult to interpret static 3D visualizations

• ggmap for maps https://github.com/dkahle/ggmap
• ggternfor ternary diagrams http://www.ggtern.com/
• ggdendro for dendrograms https://github.com/andrie/ggdendro
• ggraph for graphs/networks https://ggraph.data-imaginist.com/
• ggnetwork for graphs/networks https://briatte.github.io/ggnetwork/
• survminer for survival curves https://rpkgs.datanovia.com/survminer/index.html (one year since last update)